Point cloud feature enhancement and apparatus, computer device and storage medium

ABSTRACT

The present disclosure relates to a point cloud feature enhancement and apparatus, a computer device and a storage medium. The method includes: acquiring a three-dimensional point cloud, the three-dimensional point cloud including a plurality of input points; performing feature aggregation on neighborhood point features of the input point to obtain a first feature of the input point; mapping the first feature to an attention point corresponding to the corresponding input point; performing feature aggregation on neighborhood point features of the attention point to obtain a second feature of the corresponding input point; and performing feature fusion on the first feature and the second feature of the input point to obtain a corresponding enhanced feature. An enhancement effect of point cloud features can be improved with the method.

This application claims priority to Chinese application No.2021104526868, entitled “POINT CLOUD FEATURE ENHANCEMENT AND APPARATUS,COMPUTER DEVICE AND STORAGE MEDIUM”, filed on Apr. 26, 2021, thecontents of which are incorporated by reference herein in theirentirety.

FIELD

The present disclosure relates to the field of computer graphicstechnologies, and in particular, to a point cloud feature enhancementand apparatus, a computer device and a storage medium.

BACKGROUND

With the development of computer graphics technologies,three-dimensional vision tasks such as point cloud classification andpoint cloud segmentation based on a three-dimensional point cloud aregradually developing. Generally, after a reality scene or object isscanned by a measuring instrument such as a lidar sensor, athree-dimensional point cloud representation of the object in athree-dimensional space can be obtained, and a three-dimensional shapeof the object can be analyzed by analyzing three-dimensional point cloudfeatures. Therefore, extraction of the three-dimensional point cloudfeatures is one of the basic tasks of the three-dimensional visiontasks. Therefore, how to better extract the three-dimensional pointcloud features to achieve a better effect in subsequent visual tasksbased on the extracted three-dimensional point cloud features is aproblem worthy of attention.

Currently, the extraction of the three-dimensional point cloud featuresmainly focuses on learning of local features. Although point cloudfeature learning based on an attention mechanism exists, in a currentpoint cloud feature learning method based on the attention mechanism, aset of fixed points are required to be pre-selected as attention points,and better attention points cannot be automatically selected dependingon different three-dimensional point clouds. Therefore, such a pointcloud feature learning method has a poor enhancement effect on learnedpoint cloud features.

SUMMARY

In view of the above, there is a need to provide, with respect to theabove problem, a point cloud feature enhancement and apparatus, acomputer device and a storage medium that can improve an enhancementeffect of point cloud features.

A point cloud feature enhancement method is provided, which includes:

acquiring a three-dimensional point cloud, the three-dimensional pointcloud including a plurality of input points;

performing feature aggregation on neighborhood point features of theinput point to obtain a first feature of the input point;

mapping the first feature to an attention point corresponding to thecorresponding input point;

performing feature aggregation on neighborhood point features of theattention point to obtain a second feature of the corresponding inputpoint; and

performing feature fusion on the first feature and the second feature ofthe input point to obtain a corresponding enhanced feature.

In one embodiment, the step of mapping the first feature to an attentionpoint corresponding to the corresponding input point includes:

mapping the first feature of the input point to a target offset vector;and

determining the corresponding attention point according to the inputpoint and the target offset vector.

In one embodiment, the target offset vector is a coordinate offsetvector in a Euclidean space; and the step of determining thecorresponding attention point according to the input point and thetarget offset vector includes:

obtaining an offset point coordinate vector according to a coordinatevector of the input point in the Euclidean space and the coordinateoffset vector; and

determining the attention point corresponding to the corresponding inputpoint according to the offset point coordinate vector.

In one embodiment, the target offset vector is a feature offset vectorin a feature space; and the step of determining the correspondingattention point according to the input point and the target offsetvector includes:

obtaining an offset point feature vector according to a feature vectorof the input point in the feature space and the feature offset vector;and

determining the attention point corresponding to the corresponding inputpoint according to the offset point feature vector.

In one embodiment, the step of mapping the first feature of the inputpoint to a target offset vector includes:

mapping the first feature of each input point in the three-dimensionalpoint cloud to the corresponding target offset vector through amulti-layer perceptron sharing parameters.

In one embodiment, the step of performing feature aggregation onneighborhood point features of the attention point to obtain a secondfeature of the corresponding input point includes:

determining neighborhood points and the corresponding neighborhood pointfeatures of the attention point from the three-dimensional point cloud;and

performing feature aggregation on the determined neighborhood pointfeatures through local convolution to obtain the second feature of theinput point corresponding to the corresponding attention point.

In one embodiment, the step of determining neighborhood points and thecorresponding neighborhood point features of the attention point fromthe three-dimensional point cloud includes:

determining a preset number of neighborhood points and correspondingneighborhood point features from the three-dimensional point cloudaccording to a coordinate vector of the attention point; or

determining a preset number of neighborhood points and correspondingneighborhood point features from the three-dimensional point cloudaccording to a feature vector of the attention point.

A point cloud feature enhancement apparatus is also provided, whichincludes:

an acquisition device configured to acquire a three-dimensional pointcloud, the three-dimensional point cloud including a plurality of inputpoints;

a feature aggregation device configured to perform feature aggregationon neighborhood point features of the input point to obtain a firstfeature of the input point;

an attention point mapping device configured to map the first feature toan attention point corresponding to the corresponding input point;

the feature aggregation device being further configured to performfeature aggregation on neighborhood point features of the attentionpoint to obtain a second feature of the corresponding input point; and

a feature fusion device configured to perform feature fusion on thefirst feature and the second feature of the input point to obtain acorresponding enhanced feature.

A computer device is also provided, which includes a memory and aprocessor, the memory storing a computer program, and the processor,when executing the computer program, implements the following steps:

acquiring a three-dimensional point cloud, the three-dimensional pointcloud including a plurality of input points;

performing feature aggregation on neighborhood point features of theinput point to obtain a first feature of the input point;

mapping the first feature to an attention point corresponding to thecorresponding input point;

performing feature aggregation on neighborhood point features of theattention point to obtain a second feature of the corresponding inputpoint; and

performing feature fusion on the first feature and the second feature ofthe input point to obtain a corresponding enhanced feature.

A computer-readable storage medium is also provided, which has acomputer program stored thereon, wherein when the computer program isexecuted by a processor, the following steps are implemented:

acquiring a three-dimensional point cloud, the three-dimensional pointcloud including a plurality of input points;

performing feature aggregation on neighborhood point features of theinput point to obtain a first feature of the input point;

mapping the first feature to an attention point corresponding to thecorresponding input point;

performing feature aggregation on neighborhood point features of theattention point to obtain a second feature of the corresponding inputpoint; and

performing feature fusion on the first feature and the second feature ofthe input point to obtain a corresponding enhanced feature.

According to the point cloud feature enhancement method and apparatus,the computer device and the storage medium, after a three-dimensionalpoint cloud of a to-be-enhanced point cloud feature is acquired, foreach input point in the three-dimensional point cloud, a feature of theinput point is enhanced based on neighborhood point features of theinput point to obtain a corresponding first feature, an attention pointcorresponding to the input point is automatically learned based on thefeature-enhanced first feature, a feature of the attention point isenhanced based on neighborhood point features of the learned attentionpoint and is taken as a second feature of the corresponding input point,the feature of the input point is further enhanced by fusing the secondfeature of the input point to the corresponding first feature, and anenhanced feature with a better enhancement effect is obtained, so that apoint cloud feature with a better enhancement effect can be obtainedbased on the enhanced feature of each input point, to improve anenhancement effect of the point cloud feature.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic flowchart of a point cloud feature enhancementmethod according to an embodiment;

FIG. 2 is a schematic diagram of learning of attention pointscorresponding to input points according to an embodiment;

FIG. 3 is a schematic flowchart of a point cloud feature enhancementmethod according to another embodiment;

FIG. 4 is a frame diagram of implementation of a point cloud featureenhancement method based on a point cloud learning network according toan embodiment;

FIG. 5 is a schematic diagram of a point cloud learning networkaccording to an embodiment;

FIG. 6 is a structural block diagram of a point cloud featureenhancement apparatus according to an embodiment; and

FIG. 7 is an internal structure diagram of a computer device accordingto an embodiment.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Embodiments of the present disclosure are described in further detailbelow with reference to the accompanying drawings and embodiments. Itshould be understood that specific embodiments described herein areintended only to interpret the present disclosure and not intended tolimit the present disclosure.

In one embodiment, as shown in FIG. 1, a point cloud feature enhancementmethod is provided. The present embodiment is illustrated with anexample in which the method is applied to a terminal. It may beunderstood that the method may also be applied to a server or a systemincluding a terminal and a server, and is implemented throughinteraction between the terminal and the server. In the presentembodiment, the method includes the following steps.

In step 102, a three-dimensional point cloud is acquired, thethree-dimensional point cloud including a plurality of input points.

The three-dimensional point cloud is a set of some points in athree-dimensional space. Each point in the three-dimensional point cloudmay be understood as an input point. A point cloud feature can beenhanced by enhancing a feature of the input point.

In step 104, feature aggregation is performed on neighborhood pointfeatures of the input point to obtain a first feature of the inputpoint.

In one embodiment, for each input point in the three-dimensional pointcloud, the terminal determines a neighborhood point set of the inputpoint from the three-dimensional point cloud, acquires a neighborhoodpoint feature of each neighborhood point in the neighborhood point set,performs feature aggregation on the neighborhood point features, andupdates an initial feature of the input point based on the aggregatedfeature to obtain the first feature of the input point.

In one embodiment, the terminal performs feature aggregation onneighborhood point features of each input point through localconvolution to obtain a first feature of the corresponding input point.The terminal may specifically perform a local convolution operation onthe neighborhood point features of each input point to obtain thecorresponding first feature in the following manner.

f _(i)=LocalConv1(N(p _(i)))

where p_(i) denotes an i^(th) input point in the three-dimensional pointcloud, N(p_(i)) denotes a neighborhood point set of the input pointp_(i), f_(i) denotes a first feature of the input point p_(t), andLocalConv1 denotes any local convolution operator, which may be, forexample, a local operator for point-by-point operation, a local operatorbased on grid convolution kernel, or an attention-based local operator.The local convolution operator generally takes the neighborhood pointfeatures of the input points as input to perform feature aggregation onthe inputted neighborhood point features as the first feature of theinput point.

In one embodiment, for each input point in the three-dimensional pointcloud, the terminal calculates distances between the input point andother input points in the three-dimensional point cloud, and screensneighborhood points of the input point based on the distances to obtaina corresponding neighborhood point set. The neighborhood points of theinput point are screened from the three-dimensional point cloud based onthe distances in, but not limited to, the following manner. The terminalmay screen, from the three-dimensional point cloud, the input pointswith distances less than or equal to a preset distance threshold toserve as the neighborhood points. In this way, by taking a ball centeras an input point and a ball with a radius of the preset distancethreshold as a query ball, input points within a range of the query ballare screened from the three-dimensional point cloud to serve as theneighborhood points. It may be understood that, in the neighborhoodpoint screening manner, no neighborhood point may exist in the queryball due to a too large target offset vector of the input point. In thiscase, during automatic learning of the target offset vector based on thefirst feature, a penalty is further required to be added to a lossfunction to prevent a too large target offset vector. The terminal mayalso screen, from the three-dimensional point cloud, a preset number ofinput points with a minimum distance to serve as the neighborhoodpoints. In this way, a preset number of input points closest to theinput point are screened from the three-dimensional point cloud based onthe distances to serve as the neighborhood points. It may be understoodthat the preset distance threshold and the preset number may becustomized as required. The distance between the input points may be avector distance calculated based on coordinate vectors or featurevectors of the input points.

In step 106, the first feature is mapped to an attention pointcorresponding to the corresponding input point.

In one embodiment, for each input point in the three-dimensional pointcloud, the terminal performs feature learning based on the first featureof the input point, and determines an attention point of the input pointbased on a learned feature, to map the first feature of the input pointto the corresponding input point. In this way, the terminal canautomatically learn the attention point of each input point based on thefirst feature of the input point.

In one embodiment, the terminal maps the first feature of each inputpoint to the corresponding input point by means of a multi-layerperceptron. It may be understood that the multi-layer perceptron isparameter-sharing among the input points.

In one embodiment, the terminal learns an offset point of the inputpoint based on the first feature of the input point, and searches thethree-dimensional point cloud for an input point closest to the offsetpoint as the attention point. In this way, the terminal learns theoffset point based on the first feature of the input point, andsearches, under the guidance of the offset point, the three-dimensionalpoint cloud for the input point closest to the offset point as theattention point related to the corresponding input point.

It may be understood that, when the three-dimensional point cloud issearched for the attention point of the corresponding input point basedon the offset point, a distance between the offset point and each inputpoint in the three-dimensional point cloud is required to be calculated,and when the three-dimensional point cloud is further searched forneighborhood points of the attention point to determine a second featureof the corresponding input point based on the found neighborhood points,a distance between the attention point and each input point in thethree-dimensional point cloud is required to be calculated. Therefore,during the determination of the second feature of the correspondinginput point based on the offset point of the input point, each inputpoint in the three-dimensional point cloud is required to be traversedtwice, and a distance between the traversed input point and the offsetpoint/attention point is calculated; that is, two groups of distancesare required to be calculated. In order to reduce calculation costscaused by calculation of the two groups of distances, the offset pointcan be directly regarded as the attention point, and the second featureof the corresponding input point can be determined based on neighborhoodpoint features of the offset point, so that when the feature of thecorresponding input point is further enhanced based on the secondfeature, the calculation costs can be reduced and the processingefficiency can be improved in a case where a feature enhancement effectis ensured.

In step 108, feature aggregation is performed on neighborhood pointfeatures of the attention point to obtain a second feature of thecorresponding input point.

In one embodiment, for the attention point corresponding to each inputpoint, the terminal determines a neighborhood point set of the attentionpoint from the three-dimensional point cloud, acquires a neighborhoodpoint feature of each neighborhood point in the neighborhood point set,performs feature aggregation on the neighborhood point features, updatesa feature of the corresponding attention point based on the aggregatedfeature, and takes the updated feature of the attention point as thesecond feature of the corresponding input point.

In step 110, feature fusion is performed on the first feature and thesecond feature of the input point to obtain a corresponding enhancedfeature.

In one embodiment, for each input point in the three-dimensional pointcloud, the terminal fuses the second feature of the input point to thecorresponding first feature to further enhance the feature of the inputpoint to obtain the corresponding enhanced feature. It may be understoodthat the terminal may fuse the first feature and the second feature ofeach input point by using an existing feature fusion operation, which isnot specifically limited herein.

In one embodiment, the terminal performs feature fusion on the firstfeature and the second feature of each input point through a fusionfunction I to obtain the corresponding enhanced feature, which mayspecifically refer to the following formula:

f′ _(i) =I(f _(p) _(i) ,f _(q) _(i) )

where f′_(i) denotes an enhanced feature of the i^(th) input pointp_(i), f_(p) _(i) denotes a first feature of the input point p_(i), thatis, f_(i), and f_(q) _(i) denotes a second feature of the input pointp_(i).

In one embodiment, the terminal adds the first feature and the secondfeature of the input point through a fusion function

to obtain the enhanced feature, which may specifically refer to thefollowing formula: where

denotes a feature summing operation:

I=Add(f _(p) _(i) ,f _(q) _(i) )

In one embodiment, the terminal connects the first feature and thesecond feature of the input point through a fusion function

and reduces a dimension through a multi-layer perceptron to obtain theenhanced feature, which may specifically refer to the following formula:where Concatenate denotes a feature concatenate operation, and MLPdenotes reduction of a feature dimension through the multi-layerperceptron.

I=MLP*Cocatenate(f _(p) _(i) ,f _(q) _(i) ))

According to the point cloud feature enhancement method, after athree-dimensional point cloud of a to-be-enhanced point cloud feature isacquired, for each input point in the three-dimensional point cloud, afeature of the input point is enhanced based on neighborhood pointfeatures of the input point to obtain a corresponding first feature, anattention point corresponding to the input point is automaticallylearned based on the feature-enhanced first feature, a feature of theattention point is enhanced based on neighborhood point features of thelearned attention point and is taken as a second feature of thecorresponding input point, the feature of the input point is furtherenhanced by fusing the second feature of the input point to thecorresponding first feature, and an enhanced feature with a betterenhancement effect is obtained, so that a point cloud feature with abetter enhancement effect can be obtained based on the enhanced featureof each input point, to improve an enhancement effect of the point cloudfeature.

In one embodiment, step 106 includes: mapping the first feature of theinput point to a target offset vector; and determining the correspondingattention point according to the input point and the target offsetvector.

The target offset vector is a direction vector automatically learnedbased on the first feature of the input point. In this way, theattention point determined based on the target offset vector and theinput point is referred to as a directed attention point.

In one embodiment, the terminal learns the target offset vector of theinput point based on the first feature of the input point, anddetermines the corresponding attention point according to an input pointvector corresponding to the input point and the target offset vector. Inthis way, the terminal maps the first feature of the input point to thetarget offset vector, and takes it as a directional attention forfurther positioning the attention point. It may be understood that theinput point vector may be a coordinate vector or a feature vector.Correspondingly, the target offset vector may be a coordinate offsetvector or a feature offset vector.

In one embodiment, the terminal learns a target function D, maps thefirst feature of the input point to the corresponding target offsetvector through the target function, determines a corresponding offsetpoint based on the target offset vector and the input point, and takesthe offset point as an attention point of the corresponding input point,which may specifically be shown by the following formula.

d _(i) =D(f _(i))

where f_(i) denotes a first feature of the input point p_(t), and d_(t)denotes an offset point of the input point p_(i).

FIG. 2 is a schematic diagram of learning of attention pointscorresponding to input points according to an embodiment. As shown bySubfigure (1) in FIG. 2, for the input point p_(i) in thethree-dimensional point cloud, the terminal updates an initial featureof the input point based on neighborhood point features corresponding toneighborhood points in a neighborhood of the input point to obtain acorresponding first feature. A target offset vector of the input pointis learned based on the first feature. The target offset vector is shownby an arrow between the input point p_(i) and the offset point d_(i) inSubfigure (2). The offset point d_(i) as shown in Subfigure (2) isdetermined based on the input point and the target offset vector. Thethree-dimensional point cloud is searched, based on the offset pointd_(i), for an attention point q_(i) as shown in Subfigure (2). As shownin Subfigure (3), the three-dimensional point cloud is searched forneighborhood point features in a neighborhood of the attention pointq_(i), and a feature of the attention point is updated based on theneighborhood point features. As shown in Subfigure (4), the offset pointis automatically learned based on the first feature of the input point,and the attention point associated with the input point is found throughthe assistance of the offset point. The feature of the attention pointmay affect the feature of the input point. Therefore, the feature of theattention point obtained by updating based on the neighborhood pointfeatures is taken as a second feature of the input point p_(i), and thesecond feature is fused with the corresponding first feature to obtainan enhanced feature of the input point p_(i). It may be understood thata range represented by the reference sign 20 in FIG. 2 refers to aneighborhood of the input point, the offset point or the attentionpoint. The neighborhood range shown in FIG. 2 is only used as an exampleonly, and is not used as a specific limitation.

In the above embodiment, the target offset vector is automaticallylearned based on the first feature of the first point, to determine acorresponding attention point based on the target offset vector. In thisway, the attention point of the input point is automatically learned,independent of feature similarity, so that the enhancement effect can beimproved when the feature of the input point is further enhanced basedon the neighborhood point features of the attention point.

In one embodiment, the target offset vector is a coordinate offsetvector in a Euclidean space; and the step of determining thecorresponding attention point according to the input point and thetarget offset vector includes: obtaining an offset point coordinatevector according to a coordinate vector of the input point in theEuclidean space and the coordinate offset vector; and determining theattention point corresponding to the corresponding input point accordingto the offset point coordinate vector.

The coordinate vector of the input point in the Euclidean space isdetermined by a three-dimensional coordinate of the input point in thethree-dimensional space. For example, if the three-dimensionalcoordinate of the input point in the three-dimensional space is (x,y,z)the coordinate vector of the input point in the Euclidean space is[x,y,z]. The coordinate offset vector is a three-dimensional vector inthe Euclidean space.

In one embodiment, the terminal maps the first feature of the inputpoint to the corresponding coordinate offset vector in the Euclideanspace, adds the coordinate offset vector with the coordinate vector ofthe corresponding input point in the Euclidean space to obtain theoffset point coordinate vector, and determines the correspondingattention point in the Euclidean space according to the offset pointcoordinate vector.

In one embodiment, the terminal determines an offset point coordinatebased on the offset point coordinate vector, and determines theattention point based on the offset point coordinate in the Euclideanspace. It may be understood that, the terminal may determine the offsetpoint coordinate as a coordinate of a to-be-searched attention point.Thus, the attention point can be directly determined based on the offsetpoint coordinate; that is, the offset point is directly determined asthe attention point. The terminal may also determine the correspondingoffset point in the Euclidean space based on the offset pointcoordinate, and screen, from the three-dimensional point cloud, an inputpoint closest to the offset point to serve as the attention point in theEuclidean space. It may be understood that, the distance according tothe present embodiment refers to a vector distance between thecoordinate vector of the input point and the offset point coordinatevector.

In the above embodiment, the corresponding coordinate offset vector islearned in the Euclidean space based on the first feature of the inputpoint, and the attention point of the corresponding input point isdetermined in the Euclidean space based on the coordinate offset vector.

In one embodiment, the target offset vector is a feature offset vectorin a feature space; and the step of determining the correspondingattention point according to the input point and the target offsetvector includes: obtaining an offset point feature vector according to afeature vector of the input point in the feature space and the featureoffset vector; and determining the attention point corresponding to thecorresponding input point according to the offset point feature vector.

The feature vector of the input point in the feature space is determinedfrom a feature of the input point in the feature space, and may bespecifically determined from the first feature of the input point in thefeature space. For example, if the first feature f_(i) of the inputpoint p_(i) in the feature space is (f_(i0), f_(i1), . . . , f_(in)),the feature vector of the input point in the feature space is [f_(i0),f_(i1), . . . , f_(in)]. The feature offset vector is amulti-dimensional vector in the feature space, and the feature offsetvector has a same dimension as the first feature.

In one embodiment, the terminal maps the first feature of the inputpoint to the corresponding feature offset vector in the feature space,adds the feature offset vector with the feature vector of thecorresponding input point in the feature space to obtain the offsetpoint feature vector, and determines the corresponding attention pointin the feature space according to the offset point feature vector.

In one embodiment, the terminal determines an offset point feature basedon the offset point feature vector, and determines the attention pointbased on the offset point feature in the feature space. The terminal maydetermine the offset point feature as a feature of the to-be-searchedattention point. Thus, the attention point can be directly determinedbased on the offset point feature; that is, the offset point is directlydetermined as the attention point. The terminal may also determine thecorresponding offset point in the feature space based on the offsetpoint feature, and screen, from the three-dimensional point cloud, aninput point closest to the offset point to serve as the attention pointin the feature space. It may be understood that, the distance accordingto the present embodiment refers to a vector distance between thefeature vector of the input point and the offset point feature vector.

In the above embodiment, the corresponding feature offset vector islearned in the feature space based on the first feature of the inputpoint, and the attention point of the corresponding input point isdetermined in the feature space based on the feature offset vector.

In one embodiment, the step of mapping the first feature of the inputpoint to a target offset vector includes: mapping the first feature ofeach input point in the three-dimensional point cloud to thecorresponding target offset vector through a multi-layer perceptronsharing parameters.

In one embodiment, for each input point in the three-dimensional pointcloud, the terminal maps the first feature of the input point to thetarget offset vector through the multi-layer perceptron sharingparameters. In this way, the multi-layer perceptron configured to mapthe first feature of each input point to the corresponding target offsetvector is parameter-sharing; that is, the multi-layer perceptron sharesparameters among all input points in the three-dimensional point cloud.

In one embodiment, in the Euclidean space, the terminal maps the firstfeature of each input point to a coordinate offset vector through asmall-sized multi-layer perceptron, and further determines thecorresponding attention point based on the coordinate offset vector. Theterminal may specifically determine an offset point corresponding to theinput point in the Euclidean space in the following manner, anddetermines the corresponding attention point based on the offset pointwith the attention point determination method according to one or moreembodiments in the present disclosure.

d _(i)=MLP(f _(i))+x _(i)

where f_(i) denotes a first feature of the input point p_(i), d_(i)denotes an offset point of the input point p_(t) in the Euclidean space,MLP(f_(i)) denotes a coordinate offset vector obtained by mapping thefirst feature f_(i) in the Euclidean space through the multi-layerperceptron MLP, and x_(i) denotes a coordinate vector of the input pointp_(i) in the Euclidean space.

It may be understood that, the attention point learned in the Euclideanspace is not necessarily a certain point in the three-dimensional pointcloud, which may be at any position in the Euclidean space.

In one embodiment, in the feature space, the terminal maps the firstfeature of each input point to a feature offset vector through thesmall-sized multi-layer perceptron. The terminal may specificallydetermine an offset point corresponding to the input point in thefeature space in the following manner, and further determines theattention point based on the offset point.

d _(f) _(i) =MLP(f _(i))+f _(i)

where f_(i) denotes a first feature of the input point p_(i), that is,denotes a feature vector of the input point p_(i) in the feature space,d_(f) _(i) denotes an offset point of the input point p_(i) in thefeature space, and MLP(f_(i)) denotes a feature offset vector obtainedby mapping the first feature f_(i) in the feature space through themulti-layer perceptron MLP.

It may be understood that, the feature offset vector learned in thefeature space is not necessarily a feature vector of a certain point inthe three-dimensional point cloud, and the attention point learned inthe feature space is not necessarily a certain point in thethree-dimensional point cloud, which may be at any position in thefeature space.

In one embodiment, step 108 includes: determining neighborhood pointsand the corresponding neighborhood point features of the attention pointfrom the three-dimensional point cloud; and performing featureaggregation on the determined neighborhood point features through localconvolution to obtain the second feature of the input pointcorresponding to the corresponding attention point.

In one embodiment, for the attention point corresponding to each inputpoint in the three-dimensional point cloud, the terminal calculates adistance between the attention point and each input point in thethree-dimensional point cloud, screens neighborhood points of theattention point from the three-dimensional point cloud based on thedistance, and acquires a neighborhood point feature corresponding toeach neighborhood point. Further, local convolution is performed onneighborhood point features corresponding to the attention point toaggregate the neighborhood point features, and the aggregated feature istaken as the second feature of the input point corresponding to theattention point. It is to be noted that, similar to the manner ofscreening the neighborhood points of the input point from thethree-dimensional point cloud based on the distances, the terminalscreens the neighborhood points of the attention point from thethree-dimensional point cloud based on the distances, which is notdescribed in detail herein.

In one embodiment, the terminal performs feature aggregation onneighborhood point features of each attention point through localconvolution to obtain a second feature of the corresponding input point.The terminal may specifically perform a local convolution operation onthe neighborhood point features of each attention point to obtain thesecond feature of the corresponding input point in the following manner.

f _(q) _(i) =LocalConv2(N(q _(i)))

where q_(i) denotes an attention point corresponding to the i^(th) inputpoint p_(i) in the three-dimensional point cloud, N(q_(i)) denotes aneighborhood point set of the attention point q_(i), f_(q) _(i) denotesa second feature of the input point p_(i), and LocalConv2 denotes anylocal convolution operator. It may be understood that LocalConv1 andLocalConv2 may be identical or different, but they do not shareparameters.

In the above embodiment, the feature of the corresponding attentionpoint is updated based on the neighborhood points and the correspondingneighborhood point features of the attention point in thethree-dimensional space, and serves as the second feature of thecorresponding input point, so that the feature enhancement feature canbe improved when the corresponding first feature is enhanced based onthe second feature of the input point.

In one embodiment, the step of determining neighborhood points and thecorresponding neighborhood point features of the attention point fromthe three-dimensional point cloud includes: determining a preset numberof neighborhood points and corresponding neighborhood point featuresfrom the three-dimensional point cloud according to a coordinate vectorof the attention point; or determining a preset number of neighborhoodpoints and corresponding neighborhood point features from thethree-dimensional point cloud according to a feature vector of theattention point.

In one embodiment, in the Euclidean space, each input point in thethree-dimensional point cloud and the corresponding attention pointcorrespond to coordinate vectors respectively. For each attention point,a vector distance between the attention point and each input point inthe three-dimensional point cloud can be determined according to thecoordinate vector of the attention point and the coordinate vector ofthe corresponding input point. In the feature space, each input point inthe three-dimensional point cloud and the corresponding attention pointcorrespond to feature vectors respectively. For each attention point, avector distance between the attention point and each input point in thethree-dimensional point cloud can be determined according to the featurevector of the attention point and the feature vector of thecorresponding input point. Thus, in the Euclidean space or the featurespace, a preset number of input points can be screened out, based on thevector distance corresponding to each attention point, from thethree-dimensional point cloud as neighborhood points of the attentionpoint, and neighborhood point features corresponding to the neighborhoodpoints are acquired. It may be understood that, for the attention pointin the Euclidean space, neighborhood points of the attention point arescreened in the Euclidean space; for the attention point in the featurespace, neighborhood points of the attention point are screened in thefeature space.

It may be understood that, the coordinate vector corresponding to theattention point/input point in the Euclidean space is determined basedon a three-dimensional coordinate of the attention point/input point inthe Euclidean space. Correspondingly, the feature vector correspondingto the attention point/input point in the feature space is determinedbased on a feature of the attention point/input point in the featurespace. A dimension of the feature vector is determined by a number ofchannels of the feature.

In one embodiment, in the Euclidean space or the feature space, theterminal may determine the neighborhood points of the attention point inan existing KNN (K-Nearest Neighbor) manner, which is not described indetail herein.

In the above embodiment, the neighborhood points and the neighborhoodpoint features of the attention point can be determined from thecorresponding space based on the coordinate vector or the feature vectorof the attention point, to further enhance the feature of thecorresponding input point based on the determined neighborhood pointfeatures.

FIG. 3 is a schematic flowchart of a point cloud feature enhancementmethod according to another embodiment. As shown in FIG. 3, the methodspecifically includes the following steps.

In step 302, a three-dimensional point cloud is acquired, thethree-dimensional point cloud including a plurality of input points.

In step 304, feature aggregation is performed on neighborhood pointfeatures of the input point to obtain a first feature of the inputpoint.

In step 306, the first feature of each input point in thethree-dimensional point cloud is mapped to the corresponding targetoffset vector through a multi-layer perceptron sharing parameters; thetarget offset vector is a coordinate offset vector in a Euclidean spaceor a feature offset vector in a feature space.

In step 308, an offset point coordinate vector is obtained according toa coordinate vector of the input point in the Euclidean space and thecoordinate offset vector.

In step 310, the attention point corresponding to the correspondinginput point is determined according to the offset point coordinatevector.

In step 312, a preset number of neighborhood points and correspondingneighborhood point features are determined from the three-dimensionalpoint cloud according to a coordinate vector of the attention point.

In step 314, an offset point feature vector is obtained according to afeature vector of the input point in the feature space and the featureoffset vector.

In step 316, the attention point corresponding to the correspondinginput point is determined according to the offset point feature vector.

In step 318, a preset number of neighborhood points and correspondingneighborhood point features are determined from the three-dimensionalpoint cloud according to a feature vector of the attention point.

In step 320, feature aggregation is performed on the determinedneighborhood point features through local convolution to obtain thesecond feature of the input point corresponding to the correspondingattention point.

In step 322, feature fusion is performed on the first feature and thesecond feature of the input point to obtain a corresponding enhancedfeature.

In the above embodiment, the corresponding target offset vector islearned based on the first feature of the input point in the Euclideanspace or the feature space, and an attention point more conducive to asubsequent ask is positioned from the three-dimensional point cloudbased on the target offset vector, to enhance learned point cloudfeatures, so that the accuracy can be improved when the learned pointcloud features are used for the subsequent task such as point cloudsegmentation or point cloud classification.

In one embodiment, the point cloud feature enhancement method accordingto one or more embodiments of the present disclosure is implemented by apoint cloud learning network. The point cloud learning network is a deepneural network based on an attention mechanism. Since a main function ofthe point cloud learning network is to automatically learn a directedattention point of an input point, the point cloud learning network mayalso be understood as a directed attention point convolutional network.

FIG. 4 is a frame diagram of implementation of a point cloud featureenhancement method based on a point cloud learning network according toan embodiment. As shown in FIG. 4, a network feature abstraction layerof the point cloud learning network is provided, which specificallyincludes: taking an initial feature of each input point in thethree-dimensional point cloud as an input feature of the point cloudlearning network, updating the initial feature of each input pointthrough a local convolution device to obtain a corresponding firstfeature, mapping the first feature of each input point to acorresponding attention point through an attention point mapping device,obtaining a second feature of the corresponding input point byaggregating neighborhood point features from a neighborhood of theattention point through an attention feature aggregation device,integrating the second feature of each input point into thecorresponding first feature through an attention point feature fusiondevice to obtain an enhanced feature, and taking the enhanced feature ofeach input point as an output feature of the input point in the pointcloud learning network.

FIG. 5 is a schematic diagram of a point cloud learning networkaccording to an embodiment. Subfigure (1) and Subfigure (2) in FIG. 5provide schematic diagrams of point cloud learning networks in aEuclidean space and a feature space respectively. As shown by Subfigure(1) and Subfigure (2), the point cloud learning networks in theEuclidean space and the feature space are of a similar structure. Athree-dimensional point cloud inputted to the point cloud learningnetwork includes n input points. A feature dimension of each input pointis C1, and a coordinate dimension of each input point is 3. For eachinput point in the three-dimensional point cloud, the input point istaken as a query point to search the three-dimensional point cloud forneighborhood points and corresponding neighborhood point features of theinput point by k-neighborhood. Local convolution is performed on theneighborhood point features to obtain a first feature of the inputpoint. The first feature of the input point is mapped to a correspondingtarget offset vector through a multi-layer perceptron sharingparameters. An attention point of the input point is determinedaccording to the input point and the target offset vector. The attentionpoint is taken as a query point to search the three-dimensional pointcloud for neighborhood points and corresponding neighborhood pointfeatures of the attention point by k-neighborhood. Local convolution isperformed on the neighborhood point features to obtain a second featureof the corresponding input point. Feature fusion is performed on thefirst feature and the second feature of the input point to obtain anenhanced feature. Feature dimensions of the first feature, the secondfeature and the enhanced feature corresponding to each input point areall C2.

Differences between the point cloud learning networks in the Euclideanspace and the feature space are as follows. In the Euclidean space, thefirst feature of the input point is mapped to a three-dimensional targetoffset vector through the multi-layer perceptron sharing parameters;that is, the first feature is mapped to a coordinate offset vector, andthe attention point is determined based on the coordinate offset vectorand a coordinate vector of the input point in the Euclidean space. Inthe feature space, the first feature of the input point is mapped to afeature offset vector having a same dimension as the first feature, andthe attention point is determined based on the feature offset vector anda coordinate vector of the input point in the feature space. It may beunderstood that, during the enhancement of the feature of the inputpoint through point cloud learning network, the feature dimension may bechanged, but the coordinate dimension may remain unchanged. In this way,the target offset vector of each input point is learned in the Euclideanspace or the feature space through the point cloud learning network, anattention point more conducive to a subsequent ask is positioned fromthe three-dimensional point cloud based on the target offset vector, andlearned point cloud features are enhanced based on the attention point.

It may be understood that, for each input point in the three-dimensionalpoint cloud, an attention point is learned through the point cloudlearning network according to the present disclosure, and the feature ofthe input point can be effectively enhanced based on the attentionpoint, to improve a feature enhancement effect of the point cloudfeatures. When the attention point of the input point is learned throughthe point cloud learning network, a position of the attention point islearned in a targeted manner according to different three-dimensionalpoint clouds and different subsequent tasks. Moreover, the learnedattention point is not necessarily a certain input point in thethree-dimensional point cloud. In this way, the feature of the inputpoint is further enhanced based on neighborhood point features of theattention point, so that neighborhood points with different featuresprovide an important context for the input point to perform a targettask. It is to be noted that the learned target offset vector isoptimized by maximizing the performance of the task in a training pointcloud learning network, to learn a better position of the attentionpoint.

In one embodiment, various experiments show that the point cloudlearning network according to the present disclosure can be integratedinto various point cloud classification and segmentation networks as asub-network, and has an improved effect. The point cloud learningnetwork according to the present disclosure has been benchmarked againsta variety of common data sets, such as a ModelNet40 data set for pointcloud classification, a ShapeNetPart data set for point cloud componentsegmentation and an S3DIS data set for point cloud indoor scene semanticsegmentation. A large number of model experiments on such data sets showthat the point cloud learning network according to the presentdisclosure can improve the feature enhancement effect compared with theexisting feature enhancement method.

It is to be understood that, although the steps in the flowcharts ofFIG. 1 and FIG. 3 are displayed in sequence as indicated by the arrows,the steps are not necessarily performed in the order indicated by thearrows. Unless otherwise clearly specified herein, the steps areperformed without any strict sequence limitation, and may be performedin other orders. In addition, at least some steps in FIG. 1 and FIG. 3may include a plurality of steps or a plurality of stages, and the stepsor stages are not necessarily performed at a same moment, and may beperformed at different moments. The steps or stages are not necessarilyperformed in sequence, and may be performed in turn or alternately withat least some of other steps or steps or stages of other steps.

In one embodiment, as shown in FIG. 6, a point cloud feature enhancementapparatus 600 is provided, including: an acquisition device 601, afeature aggregation device 602, an attention point mapping device 603and a feature fusion device 604.

The acquisition device 601 is configured to acquire a three-dimensionalpoint cloud, the three-dimensional point cloud including a plurality ofinput points.

The feature aggregation device 602 is configured to perform featureaggregation on neighborhood point features of the input point to obtaina first feature of the input point.

The attention point mapping device 603 is configured to map the firstfeature to an attention point corresponding to the corresponding inputpoint.

The feature aggregation device 602 is further configured to performfeature aggregation on neighborhood point features of the attentionpoint to obtain a second feature of the corresponding input point.

The feature fusion device 604 is configured to perform feature fusion onthe first feature and the second feature of the input point to obtain acorresponding enhanced feature.

In one embodiment, the attention point mapping device 603 is furtherconfigured to map the first feature of the input point to a targetoffset vector; and determine the corresponding attention point accordingto the input point and the target offset vector.

In one embodiment, the target offset vector is a coordinate offsetvector in a Euclidean space; and the attention point mapping device 603is further configured to obtain an offset point coordinate vectoraccording to a coordinate vector of the input point in the Euclideanspace and the coordinate offset vector; and determine the attentionpoint corresponding to the corresponding input point according to theoffset point coordinate vector.

In one embodiment, the target offset vector is a feature offset vectorin a feature space; and the attention point mapping device 603 isfurther configured to obtain an offset point feature vector according toa feature vector of the input point in the feature space and the featureoffset vector; and determine the attention point corresponding to thecorresponding input point according to the offset point feature vector.

In one embodiment, the attention point mapping device 603 is furtherconfigured to map the first feature of each input point in thethree-dimensional point cloud to the corresponding target offset vectorthrough a multi-layer perceptron sharing parameters.

In one embodiment, the feature aggregation device 602 is furtherconfigured to determine neighborhood points and the correspondingneighborhood point features of the attention point from thethree-dimensional point cloud; and perform feature aggregation on thedetermined neighborhood point features through local convolution toobtain the second feature of the input point corresponding to thecorresponding attention point.

In one embodiment, the feature aggregation device 602 is furtherconfigured to determine a preset number of neighborhood points andcorresponding neighborhood point features from the three-dimensionalpoint cloud according to a coordinate vector of the attention point; ordetermine a preset number of neighborhood points and correspondingneighborhood point features from the three-dimensional point cloudaccording to a feature vector of the attention point.

The specific limitation to the point cloud feature enhancement apparatusmay be obtained with reference to the limitation to the point cloudfeature enhancement method hereinabove, and is not described in detailherein. The devices in the point cloud feature enhancement apparatus maybe implemented entirely or partially by software, hardware, or acombination thereof. The above devices may be built in or independent ofa processor of a computer device in a hardware form, or may be stored ina memory of the computer device in a software form, so that theprocessor invokes and performs operations corresponding to the abovedevices.

In one embodiment, a computer device is provided. The computer devicemay be a terminal, and an internal structure diagram is shown in FIG. 7.The computer device includes a processor, a memory, a communicationinterface, a display screen, and an input apparatus that are connectedby using a system bus. The processor of the computer device isconfigured to provide computing and control capabilities. The memory ofthe computer device includes a non-transitory storage medium and aninternal memory. The non-transitory storage medium stores an operatingsystem and a computer program. The internal memory provides anenvironment for running of the operating system and the computer programin the non-transitory storage medium. The communication interface of thecomputer device is configured to communicate with an external terminalin a wired or wireless manner. The wireless manner may be implemented byWIFI, a service provider network, NFC (Near field communication) orother technologies. The computer program is executed by the processor toimplement a point cloud feature enhancement method. The display screenof the computer device may be a liquid crystal display screen or anelectronic ink display screen. The input apparatus of the computerdevice may be a touchscreen covering the display screen, or may be akey, a trackball, or a touchpad disposed on a housing of the computerdevice, or may be an external keyboard, a touchpad, a mouse, or thelike.

In one embodiment, in the structure shown in FIG. 7, only a blockdiagram of a partial structure related to the solution in the presentdisclosure is shown, which does not constitute a limitation to thecomputer device to which the solution in the present disclosure isapplied. In one embodiment, the computer device may include more orfewer components than those shown in the figure, or some components maybe combined, or a different component deployment may be used.

In one embodiment, a computer device is provided, including a memory anda processor. The memory stores a computer program. The processor, whenexecuting the computer program, implements the following steps:acquiring a three-dimensional point cloud, the three-dimensional pointcloud including a plurality of input points; performing featureaggregation on neighborhood point features of the input point to obtaina first feature of the input point; mapping the first feature to anattention point corresponding to the corresponding input point;performing feature aggregation on neighborhood point features of theattention point to obtain a second feature of the corresponding inputpoint; and performing feature fusion on the first feature and the secondfeature of the input point to obtain a corresponding enhanced feature.

In one embodiment, the processor, when executing the computer program,further implements the following steps: mapping the first feature of theinput point to a target offset vector; and determining the correspondingattention point according to the input point and the target offsetvector.

In one embodiment, the target offset vector is a coordinate offsetvector in a Euclidean space; and the processor, when executing thecomputer program, further implements the following steps: obtaining anoffset point coordinate vector according to a coordinate vector of theinput point in the Euclidean space and the coordinate offset vector; anddetermining the attention point corresponding to the corresponding inputpoint according to the offset point coordinate vector.

In one embodiment, the target offset vector is a feature offset vectorin a feature space; and the processor, when executing the computerprogram, further implements the following steps: obtaining an offsetpoint feature vector according to a feature vector of the input point inthe feature space and the feature offset vector; and determining theattention point corresponding to the corresponding input point accordingto the offset point feature vector.

In one embodiment, the processor, when executing the computer program,further implements the following step: mapping the first feature of eachinput point in the three-dimensional point cloud to the correspondingtarget offset vector through a multi-layer perceptron sharingparameters.

In one embodiment, the processor, when executing the computer program,further implements the following steps: determining neighborhood pointsand the corresponding neighborhood point features of the attention pointfrom the three-dimensional point cloud; and performing featureaggregation on the determined neighborhood point features through localconvolution to obtain the second feature of the input pointcorresponding to the corresponding attention point.

In one embodiment, the processor, when executing the computer program,further implements the following steps: determining a preset number ofneighborhood points and corresponding neighborhood point features fromthe three-dimensional point cloud according to a coordinate vector ofthe attention point; or determining a preset number of neighborhoodpoints and corresponding neighborhood point features from thethree-dimensional point cloud according to a feature vector of theattention point.

In one embodiment, a computer-readable storage medium is provided,having a computer program stored thereon. When the computer program isexecuted by a processor, the following steps are implemented: acquiringa three-dimensional point cloud, the three-dimensional point cloudincluding a plurality of input points; performing feature aggregation onneighborhood point features of the input point to obtain a first featureof the input point; mapping the first feature to an attention pointcorresponding to the corresponding input point; performing featureaggregation on neighborhood point features of the attention point toobtain a second feature of the corresponding input point; and performingfeature fusion on the first feature and the second feature of the inputpoint to obtain a corresponding enhanced feature.

In one embodiment, when the computer program is executed by theprocessor, the following steps are further implemented: mapping thefirst feature of the input point to a target offset vector; anddetermining the corresponding attention point according to the inputpoint and the target offset vector.

In one embodiment, the target offset vector is a coordinate offsetvector in a Euclidean space; and when the computer program is executedby the processor, the following steps are further implemented: obtainingan offset point coordinate vector according to a coordinate vector ofthe input point in the Euclidean space and the coordinate offset vector;and determining the attention point corresponding to the correspondinginput point according to the offset point coordinate vector.

In one embodiment, the target offset vector is a feature offset vectorin a feature space; and when the computer program is executed by theprocessor, the following steps are further implemented: obtaining anoffset point feature vector according to a feature vector of the inputpoint in the feature space and the feature offset vector; anddetermining the attention point corresponding to the corresponding inputpoint according to the offset point feature vector.

In one embodiment, when the computer program is executed by theprocessor, the following step is further implemented: mapping the firstfeature of each input point in the three-dimensional point cloud to thecorresponding target offset vector through a multi-layer perceptronsharing parameters.

In one embodiment, when the computer program is executed by theprocessor, the following steps are further implemented: determiningneighborhood points and the corresponding neighborhood point features ofthe attention point from the three-dimensional point cloud; andperforming feature aggregation on the determined neighborhood pointfeatures through local convolution to obtain the second feature of theinput point corresponding to the corresponding attention point.

In one embodiment, when the computer program is executed by theprocessor, the following steps are further implemented: determining apreset number of neighborhood points and corresponding neighborhoodpoint features from the three-dimensional point cloud according to acoordinate vector of the attention point; or determining a preset numberof neighborhood points and corresponding neighborhood point featuresfrom the three-dimensional point cloud according to a feature vector ofthe attention point.

The above embodiments may be implemented by a computer-readableinstruction instructing related hardware, the program may be stored in anon-transitory computer-readable storage medium, and when the program isexecuted, the procedures in the above method embodiments may beimplemented. Any reference to a memory, a storage, a database, or othermedia used in the embodiments provided in the present disclosure mayinclude at least one of a non-transitory memory and a transitory memory.The non-transitory memory may include a read-only memory (ROM), amagnetic tape, a floppy disk, a flash memory, an optical memory or thelike. The transitory memory may include a random access memory (RAM) oran external high-speed cache memory. By way of illustration and notlimitation, the RAM is available in a variety of forms, such as a StaticRandom Access Memory (SRAM), a Dynamic Random Access Memory (DRAM) orthe like.

1. A point cloud feature enhancement method, comprising: acquiring athree-dimensional point cloud, the three-dimensional point cloudcomprising a plurality of input points; performing feature aggregationon neighborhood point features of the input point to obtain a firstfeature of the input point; mapping the first feature to an attentionpoint corresponding to the corresponding input point; performing featureaggregation on neighborhood point features of the attention point toobtain a second feature of the corresponding input point; performingfeature fusion on the first feature and the second feature of the inputpoint to obtain a corresponding enhanced feature.
 2. The methodaccording to claim 1, wherein the step of mapping the first feature toan attention point corresponding to the corresponding input pointcomprises: mapping the first feature of the input point to a targetoffset vector; determining the corresponding attention point accordingto the input point and the target offset vector.
 3. The method accordingto claim 2, wherein the target offset vector is a coordinate offsetvector in a Euclidean space; and the step of determining thecorresponding attention point according to the input point and thetarget offset vector comprises: obtaining an offset point coordinatevector according to a coordinate vector of the input point in theEuclidean space and the coordinate offset vector; determining theattention point corresponding to the corresponding input point accordingto the offset point coordinate vector.
 4. The method according to claim2, wherein the target offset vector is a feature offset vector in afeature space; and the step of determining the corresponding attentionpoint according to the input point and the target offset vectorcomprises: obtaining an offset point feature vector according to afeature vector of the input point in the feature space and the featureoffset vector; determining the attention point corresponding to thecorresponding input point according to the offset point feature vector.5. The method according to claim 2, wherein the step of mapping thefirst feature of the input point to a target offset vector comprises:mapping a first feature of each input point in the three-dimensionalpoint cloud to the corresponding target offset vector through amulti-layer perceptron sharing parameters.
 6. The method according toclaim 1, wherein the step of performing feature aggregation onneighborhood point features of the attention point to obtain a secondfeature of the corresponding input point comprises: determiningneighborhood points and the corresponding neighborhood point features ofthe attention point from the three-dimensional point cloud; performingfeature aggregation on the determined neighborhood point featuresthrough local convolution to obtain the second feature of the inputpoint corresponding to the corresponding attention point.
 7. The methodaccording to claim 6, wherein the step of determining neighborhoodpoints and the corresponding neighborhood point features of theattention point from the three-dimensional point cloud comprises:determining a preset number of neighborhood points and correspondingneighborhood point features from the three-dimensional point cloudaccording to a coordinate vector of the attention point; or determininga preset number of neighborhood points and corresponding neighborhoodpoint features from the three-dimensional point cloud according to afeature vector of the attention point.
 8. A point cloud featureenhancement apparatus, comprising: an acquisition device configured toacquire a three-dimensional point cloud, the three-dimensional pointcloud comprising a plurality of input points; a feature aggregationdevice configured to perform feature aggregation on neighborhood pointfeatures of the input point to obtain a first feature of the inputpoint; an attention point mapping device configured to map the firstfeature to an attention point corresponding to the corresponding inputpoint; the feature aggregation device being further configured toperform feature aggregation on neighborhood point features of theattention point to obtain a second feature of the corresponding inputpoint; a feature fusion device configured to perform feature fusion onthe first feature and the second feature of the input point to obtain acorresponding enhanced feature.
 9. A computer device, comprising amemory and a processor, the memory storing a computer program, whereinthe processor, when executing the computer program, implements steps ofthe method according to claim
 1. 10. A computer-readable storage medium,having a computer program stored thereon, wherein steps of the methodaccording to claim 1 are implemented when the computer program isexecuted by a processor.